Graphical Displays

• Categorical Data
  • Bar Chart
  • Pie Chart
• Quantitative Data
  • Histogram
  • Boxplot
  • Scatterplot
  • Line

Common Arguments

Here is a list of common arguments:

• col: a vector of colors
• main: title for the plot
• xlim or ylim: limits for the x or y axis
• xlab or ylab: a label for the x axis
• font: font used for text, 1=plain; 2=bold; 3=italic, 4=bold italic
• font.axis: font used for axis
• cex.axis: font size for x and y axes
• font.lab: font for x and y labels
• cex.lab: font size for x and y labels

First 500 Observations

Description

In order to understand the customer purchases behavior against various products of different categories, the retail company “ABC Private Limited”, in United Kingdom, shared purchase summary of various customers for selected high volume products from last month. The data contain the following variables.

• User_ID: User ID
• Product_ID: Product ID
• Gender: Sex of User
• Age: Age in bins
• Occupation: Occupation (Masked)
• City_Category: Category of the City (A,B,C)
• Stay_In_Current_City_Years: Number of years stay in current city
• Marital_Status: Marital Status
• Product_Category_1: Product Category (Masked)
• Product_Category_2: Product may belongs to other category also (Masked)
• Product_Category_3: Product may belongs to other category also (Masked)
• Purchase: Purchase Amount

Rows: 550,068
Columns: 12
$ User_ID                    <dbl> 1000001, 1000001, 1000001, 1000001, 1000002~
$ Product_ID                 <chr> "P00069042", "P00248942", "P00087842", "P00~
$ Gender                     <chr> "F", "F", "F", "F", "M", "M", "M", "M", "M"~
$ Age                        <chr> "0-17", "0-17", "0-17", "0-17", "55+", "26-~
$ Occupation                 <dbl> 10, 10, 10, 10, 16, 15, 7, 7, 7, 20, 20, 20~
$ City_Category              <chr> "A", "A", "A", "A", "C", "A", "B", "B", "B"~
$ Stay_In_Current_City_Years <chr> "2", "2", "2", "2", "4+", "3", "2", "2", "2~
$ Marital_Status             <dbl> 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0~
$ Product_Category_1         <dbl> 3, 1, 12, 12, 8, 1, 1, 1, 1, 8, 5, 8, 8, 1,~
$ Product_Category_2         <dbl> NA, 6, NA, 14, NA, 2, 8, 15, 16, NA, 11, NA~
$ Product_Category_3         <dbl> NA, 14, NA, NA, NA, NA, 17, NA, NA, NA, NA,~
$ Purchase                   <dbl> 8370, 15200, 1422, 1057, 7969, 15227, 19215~

Bar chart is a graphical display good for the general audience. Here, we study the distribution of Age Group of the company’s customers who purchased their products on Black Friday.

Usage: barplot(height, …)

A bar chart can be horizontal or vertical. Using the argument col, we can assign a color for bars. The argument main could be used to change the title of the figure. We can use RGB color code to assign colors.

Note: The margin of a figure could be set using the par() function. The order of the setting is c(bottom, left, top, right).

Analysis

Vertical Bar Chart

Horizontal Bar Chart

Analysis

Distribution of City Category

Histogram is used when we want to study the distribution of a quantitative variable. Here we study the distribution of customer purchase amount.

Usage: hist(x, …)

Analysis

Analysis of Boxplot 1

Analysis of Boxplot 2

When we want to study the relationship of two quantitative variables, a scatterplot can be used. Since this data set doesn’t have another quantitative variable, we will use the built-in data mtcars in R. Then we study the relationship of miles per gallon against the weight of vehicles.

Analysis

Line Chart